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In Getting Ready for Machine Learning, Tim Negris states that “despite what many business people might guess, machine learning is not in its infancy. It has come to be used very effectively across a wide array of applications.” Machine Learning is definitely one of the hottest topics within the IT industry right now, but Negris’ assessment that it isn’t exactly new is correct. Back in 2014, my team and I identified it as a top priority for our company and customers. Since then, we have developed some interesting prototypes based on our overarching strategy to embed machine learning in our products, turning them into intelligent applications.

As the Chief Innovation Officer at SAP and Head of our global Innovation Center Network – which pioneers new markets and embraces emerging technologies for the company – I am responsible for leading the innovation agenda, setting and identifying trends, delivering new growth businesses and promoting an entrepreneurial culture. This is precisely where machine learning comes into play. While it might be difficult to see the connection between culture and machine learning, I would argue that it can actually be the key to creating and sustaining an innovative culture.

Machine learning is one of SAP’s top strategic priorities, as we are dedicated to making all our enterprise applications, such as finance, customer relationship management and human resources (HR), more intelligent. In the case of HR, for example, it is of utmost importance for innovative companies to attract the world’s best talent, especially in such a competitive marketplace.

According to a study conducted by Oxford Economics and sponsored by SAP of more than 4,100 executives and employees worldwide, companies with higher revenue and profitability are more likely to have established effective diversity programs. This means that building teams of qualified employees from a variety of backgrounds is not only beneficial—it is critical to competitive survival.

A major hurdle to building a talented and passionate workforce, however, is unintentional bias. It is nearly unavoidable and an innate human aspect of the hiring process. Too often talent recruiters disregard the intrinsic value of the individual, instead prioritizing a number of culturally normative “qualifications,” such as alma mater or hometown. They sometimes ignore the fact that under the surface, highly passionate and talented people come from different walks of life and backgrounds, and the perspective they carry is the true engine of innovation shaping the future of business.

But with machine learning, computers can process and learn from large amounts of recruitment and hiring data without being explicitly programmed. While recruiting and hiring inevitably require a human touch, machines can support the process and help talent acquisition professionals ensure they are not overlooking strong candidates because of some preconceived notion of their own. Our recently developed resumé matching application makes balanced recruiting recommendations. It identifies the best candidates for a given job description or the best job for a given candidate – providing the recruiters more personal time with the candidates instead of spending that time tediously sorting through thousands of job applications manually.

Moreover, SAP announced upcoming capabilities within its human capital management suite last year. By embedding machine learning in its software, SAP will help detect unintended discrimination across multiple HR processes (such as how job descriptions or performance reviews are written) and suggest changes to encourage equity. For example, recruiters and hiring managers may find that certain job descriptions are attracting predominately male or female job applicants, but they may not realize the role they are inadvertently playing in this outcome.

Future SAP SuccessFactors Recruiting capabilities will enable recruiters to identify potentially gender biased words and phrases that are impeding their ability to attract all of the best talent, and replace them with more neutral language. Identification of biased terms will be based not only on research, which has shown that certain phrases are more likely to attract men or women, but also on words and phrases appearing in other job descriptions that have generated primarily male or female applicant pools. This is how machine learning can be applied toward creating a less biased recruiting process—by learning from mistakes we’ve made in the past.

As my team sets out to inspire and promote innovation, we understand that means we need to bring all voices to the table. Machine learning gives us an opportunity to meet this goal by overcoming biases hidden in even the best of intentions – and ensure the richest, most diverse creative minds are represented.

International Women’s Day, taking place this year on March 8, celebrates the social, economic, cultural and political achievement of women. Progress is still too slow in many places around the world so global action is needed to accelerate gender parity. Let’s continue to support women’s advancement – not only for International Women’s Day, but every day – and let’s #BeBoldForChange. See how SAP is a champion for gender equity by following the #BeBoldAtSAP hashtag on Twitter.

 

 

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